from __future__ import annotations

from math import pi
from typing import Protocol

import matplotlib.pyplot as plt
import numpy as np





class     lowerCAmelCase__     (				SCREAMING_SNAKE_CASE_			):


		'''simple docstring'''






		def   _snake_case       (       self     :     Optional[int]     ,  snake_case__     :     float		)      ->	float:
				return 0.0



def 	lowerCamelCase					(	UpperCamelCase     :				np.ndarray			,       UpperCamelCase     :				int							)   ->    tuple[int | float, int | float]:
		_lowerCamelCase				   =  min([-20, np.min(fft_results[1 : samplerate // 2 - 1]							)]							)
		_lowerCamelCase				   =  max([20, np.max(fft_results[1 : samplerate // 2 - 1]							)]							)
		return lowest, highest


def 	lowerCamelCase					(	UpperCamelCase     :				FilterType			,       UpperCamelCase     :				int							)   ->    None:
		_lowerCamelCase				   =  5_12
		_lowerCamelCase				   =  [1] + [0] * (size - 1)
		_lowerCamelCase				   =  [filter_type.process(UpperCamelCase							) for item in inputs]

		_lowerCamelCase				   =  [0] * (samplerate - size)  # zero-padding
		outputs += filler
		_lowerCamelCase				   =  np.abs(np.fft.fft(UpperCamelCase							)							)
		_lowerCamelCase				   =  20 * np.logaa(UpperCamelCase							)

		# Frequencies on log scale from 24 to nyquist frequency
		plt.xlim(24			,       samplerate / 2 - 1							)
		plt.xlabel('Frequency (Hz)'							)
		plt.xscale('log'							)

		# Display within reasonable bounds
		_lowerCamelCase				   =  get_bounds(UpperCamelCase			,       UpperCamelCase							)
		plt.ylim(max([-80, bounds[0]]							)			,       min([80, bounds[1]]							)							)
		plt.ylabel('Gain (dB)'							)

		plt.plot(UpperCamelCase							)
		plt.show()


def 	lowerCamelCase					(	UpperCamelCase     :				FilterType			,       UpperCamelCase     :				int							)   ->    None:
		_lowerCamelCase				   =  5_12
		_lowerCamelCase				   =  [1] + [0] * (size - 1)
		_lowerCamelCase				   =  [filter_type.process(UpperCamelCase							) for item in inputs]

		_lowerCamelCase				   =  [0] * (samplerate - size)  # zero-padding
		outputs += filler
		_lowerCamelCase				   =  np.angle(np.fft.fft(UpperCamelCase							)							)

		# Frequencies on log scale from 24 to nyquist frequency
		plt.xlim(24			,       samplerate / 2 - 1							)
		plt.xlabel('Frequency (Hz)'							)
		plt.xscale('log'							)

		plt.ylim(-2 * pi			,       2 * pi							)
		plt.ylabel('Phase shift (Radians)'							)
		plt.plot(np.unwrap(UpperCamelCase			,       -2 * pi							)							)
		plt.show()
A						=			'\n# Transformers installation\n! pip install transformers datasets\n# To install from source instead of the last release, comment the command above and uncomment the following one.\n# ! pip install git+https://github.com/huggingface/transformers.git\n'

A						=			[{'type': 'code', 'content': INSTALL_CONTENT}]
A						=			{
    '{processor_class}': 'FakeProcessorClass',
    '{model_class}': 'FakeModelClass',
    '{object_class}': 'FakeObjectClass',
}